class_docstr: Builds datasets from tf.data.Dataset without defining a class.
  
  This is kept for backwards-compatibility.
code: def __init__(
      self,
      *,
      split_datasets: Mapping[str, tf.data.Dataset],
      **kwargs: Any,
  ):
      logging.warning(
          "This class is deprecated. "
          "Please use tfds.dataset_builders.store_as_tfds_dataset instead."
      )
      super().__init__(split_datasets=split_datasets, **kwargs)
  
imports: from __future__ import annotations
  from collections.abc import Iterable, Mapping
  import typing
  from typing import Any, Dict
  from absl import logging
  from etils import epath
  from tensorflow_datasets.core import dataset_builder
  from tensorflow_datasets.core import dataset_info
  from tensorflow_datasets.core import dataset_utils
  from tensorflow_datasets.core import download
  from tensorflow_datasets.core import features
  from tensorflow_datasets.core import file_adapters
  from tensorflow_datasets.core import split_builder
  from tensorflow_datasets.core import splits
  from tensorflow_datasets.core import utils
  from tensorflow_datasets.core.utils.lazy_imports_utils import apache_beam
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
label_desc: Use image node_id 1 to create a new TfDataBuilder object from inherited base classes: AdhocBuilder with example: obj = TfDataBuilder()